{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:18.971264Z",
     "start_time": "2024-06-06T10:06:18.949308Z"
    }
   },
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x, y = load_digits(return_X_y=True)  #载入数据\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=202406)  #划分训练集与测试集\n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1437, 64) (360, 64) (1437,) (360,)\n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:21.196604Z",
     "start_time": "2024-06-06T10:06:21.192355Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "#对标签进行独热编码\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n"
   ],
   "id": "e96fcc0c1fa912ac",
   "outputs": [],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:23.715806Z",
     "start_time": "2024-06-06T10:06:23.677243Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras import models, layers, regularizers\n",
    "\n",
    "#定义网络结构\n",
    "DNN = models.Sequential()\n",
    "#定义各个全连接层\n",
    "DNN.add(layers.Dense(50, activation='relu', input_shape=(64,), kernel_regularizer=regularizers.l1(0.001)))\n",
    "DNN.add(layers.Dropout(0.001))\n",
    "DNN.add(layers.Dense(25, activation='relu', kernel_regularizer=regularizers.l1(0.001)))\n",
    "DNN.add(layers.Dropout(0.001))\n",
    "#输出层选择softmax函数为激活函数\n",
    "DNN.add(layers.Dense(10, activation='softmax'))"
   ],
   "id": "6bc2f3001184daf",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:26.408539Z",
     "start_time": "2024-06-06T10:06:25.620844Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#绘制网络结构示意图\n",
    "plot_model(DNN, to_file='DNN.png', show_shapes=True, show_layer_names=False,rankdir='TB')\n",
    "plt.figure(figsize=(10, 10))\n",
    "img=plt.imread('DNN.png')\n",
    "plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ],
   "id": "5d7fb829acb89ff9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:34.181332Z",
     "start_time": "2024-06-06T10:06:31.991460Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras.optimizers import RMSprop\n",
    "\n",
    "#设置训练参数并进行训练\n",
    "#设置学习率为0.001\n",
    "DNN.compile(optimizer=RMSprop(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "#进行20次训练，每批64个数据\n",
    "DNN.fit(x_train, y_train, epochs=20, batch_size=64)\n"
   ],
   "id": "720d456abf7912c5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.0655 - loss: 7.1661    \n",
      "Epoch 2/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.2647 - loss: 2.5696 \n",
      "Epoch 3/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - accuracy: 0.6420 - loss: 1.6776 \n",
      "Epoch 4/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.8421 - loss: 1.1172 \n",
      "Epoch 5/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9051 - loss: 0.8197 \n",
      "Epoch 6/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9230 - loss: 0.6870 \n",
      "Epoch 7/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9228 - loss: 0.6238 \n",
      "Epoch 8/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9261 - loss: 0.5789 \n",
      "Epoch 9/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9465 - loss: 0.5122 \n",
      "Epoch 10/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9738 - loss: 0.4514 \n",
      "Epoch 11/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9732 - loss: 0.4269 \n",
      "Epoch 12/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9741 - loss: 0.4156 \n",
      "Epoch 13/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9756 - loss: 0.3936 \n",
      "Epoch 14/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9810 - loss: 0.3780 \n",
      "Epoch 15/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9853 - loss: 0.3524 \n",
      "Epoch 16/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9869 - loss: 0.3345 \n",
      "Epoch 17/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9831 - loss: 0.3396 \n",
      "Epoch 18/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9836 - loss: 0.3328 \n",
      "Epoch 19/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9885 - loss: 0.3140 \n",
      "Epoch 20/20\n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9898 - loss: 0.3070 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x23c9f6a7c50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:38.661158Z",
     "start_time": "2024-06-06T10:06:38.214915Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "#进行模型评估\n",
    "train_loss, train_acc = DNN.evaluate(x_train, y_train, batch_size=64)\n",
    "print(f\"训练集损失函数{train_loss:.4f},准确率{train_acc * 100:.2f}%\")\n",
    "test_loss, test_acc = DNN.evaluate(x_test, y_test, batch_size=64)\n",
    "print(f\"测试集损失函数：{test_loss:.4f},准确率：{test_acc * 100:.2f}%\")\n",
    "y_pred = DNN.predict(x_test).argmax(axis=-1)\n",
    "y_true = y_test.argmax(axis=-1)\n",
    "print(\"-----分类报告如下-----\")\n",
    "print(classification_report(y_true, y_pred))\n",
    "print(\"混淆矩阵如下\")\n",
    "cm=confusion_matrix(y_true, y_pred)\n",
    "print(cm)\n",
    "print(\"-----网络报告如下-----\")\n",
    "print(DNN.summary())"
   ],
   "id": "e5acc20c564cab25",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 949us/step - accuracy: 0.9940 - loss: 0.2835\n",
      "训练集损失函数0.2876,准确率99.51%\n",
      "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - accuracy: 0.9674 - loss: 0.3796 \n",
      "测试集损失函数：0.3713,准确率：97.22%\n",
      "\u001B[1m12/12\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 3ms/step \n",
      "-----分类报告如下-----\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98        30\n",
      "           1       0.92      1.00      0.96        33\n",
      "           2       1.00      1.00      1.00        42\n",
      "           3       0.97      0.94      0.96        35\n",
      "           4       0.97      0.94      0.96        35\n",
      "           5       1.00      0.97      0.99        38\n",
      "           6       1.00      0.97      0.99        36\n",
      "           7       0.95      0.97      0.96        40\n",
      "           8       0.97      0.95      0.96        38\n",
      "           9       0.94      1.00      0.97        33\n",
      "\n",
      "    accuracy                           0.97       360\n",
      "   macro avg       0.97      0.97      0.97       360\n",
      "weighted avg       0.97      0.97      0.97       360\n",
      "\n",
      "混淆矩阵如下\n",
      "[[29  0  0  0  1  0  0  0  0  0]\n",
      " [ 0 33  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 42  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 33  0  0  0  0  1  1]\n",
      " [ 0  0  0  0 33  0  0  2  0  0]\n",
      " [ 0  1  0  0  0 37  0  0  0  0]\n",
      " [ 0  1  0  0  0  0 35  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 39  0  1]\n",
      " [ 0  1  0  1  0  0  0  0 36  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 33]]\n",
      "-----网络报告如下-----\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential_5\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_15 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m50\u001B[0m)             │         \u001B[38;5;34m3,250\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (\u001B[38;5;33mDropout\u001B[0m)            │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m50\u001B[0m)             │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_16 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m25\u001B[0m)             │         \u001B[38;5;34m1,275\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_11 (\u001B[38;5;33mDropout\u001B[0m)            │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m25\u001B[0m)             │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_17 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m10\u001B[0m)             │           \u001B[38;5;34m260\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,250</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,275</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m9,572\u001B[0m (37.39 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,572</span> (37.39 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m4,785\u001B[0m (18.69 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,785</span> (18.69 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Optimizer params: \u001B[0m\u001B[38;5;34m4,787\u001B[0m (18.70 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,787</span> (18.70 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:44.278113Z",
     "start_time": "2024-06-06T10:06:43.850535Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# 绘制混淆矩阵的热力图\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.arange(10), yticklabels=np.arange(10))\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ],
   "id": "8671a5e99e71f584",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:49.189208Z",
     "start_time": "2024-06-06T10:06:48.791675Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#随机展示一个样例\n",
    "index = np.random.randint(0, len(x_test))\n",
    "sample = x_test[index]\n",
    "sample = sample.reshape(8, 8)\n",
    "plt.imshow(sample, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.title(f\"True Label:{y_true[index]},Predicted Label:{y_pred[index]}\")\n",
    "plt.show()\n",
    "print(np.argmax(y_pred[index]))"
   ],
   "id": "786508310d968b1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-06T10:06:57.624234Z",
     "start_time": "2024-06-06T10:06:57.607004Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#测试完毕，保存网络模型\n",
    "DNN.save('DNN.h5')"
   ],
   "id": "7874334b4c51bc29",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "execution_count": 39
  }
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